by Kwong, Wang-Wai Renfrew. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 102-105). / Abstracts in English and Chinese. / Abstract --- p.ii / Acknowledgments --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Data Mining Tasks --- p.1 / Chapter 1.1.1 --- Characterization --- p.2 / Chapter 1.1.2 --- Discrimination --- p.2 / Chapter 1.1.3 --- Classification --- p.2 / Chapter 1.1.4 --- Clustering --- p.3 / Chapter 1.1.5 --- Prediction --- p.3 / Chapter 1.1.6 --- Description --- p.3 / Chapter 1.1.7 --- Association Rule Mining --- p.4 / Chapter 1.2 --- Motivation --- p.4 / Chapter 1.2.1 --- Motivation for Mining Multi-level Association Rules Using Data Cubes --- p.4 / Chapter 1.2.2 --- Motivation for Mining N-most Interesting Itemsets --- p.8 / Chapter 1.3 --- Outline of the Thesis --- p.10 / Chapter 2 --- Survey on Previous Work --- p.11 / Chapter 2.1 --- Data Warehousing --- p.11 / Chapter 2.1.1 --- Data Cube --- p.12 / Chapter 2.2 --- Data Mining --- p.13 / Chapter 2.2.1 --- Association Rules --- p.14 / Chapter 2.2.2 --- Multi-level Association Rules --- p.15 / Chapter 2.2.3 --- Multi-Dimensional Association Rules Using Data Cubes --- p.16 / Chapter 2.2.4 --- Apriori Algorithm --- p.19 / Chapter 3 --- Mining Multi-level Association Rules Using Data Cubes --- p.22 / Chapter 3.1 --- Use of Multi-level Concept --- p.22 / Chapter 3.1.1 --- Multi-level Concept --- p.22 / Chapter 3.1.2 --- Criteria of Using Multi-level Concept --- p.23 / Chapter 3.1.3 --- Use of Multi-level Concept in Association Rules --- p.24 / Chapter 3.2 --- Use of Data Cube --- p.25 / Chapter 3.2.1 --- Data Cube --- p.25 / Chapter 3.2.2 --- Mining Multi-level Association Rules Using Data Cubes --- p.26 / Chapter 3.2.3 --- Definition --- p.28 / Chapter 3.3 --- Method for Mining Multi-level Association Rules Using Data Cubes --- p.31 / Chapter 3.3.1 --- Algorithm --- p.33 / Chapter 3.3.2 --- Example --- p.35 / Chapter 3.4 --- Experiment --- p.44 / Chapter 3.4.1 --- Simulation of Data Cube by Array --- p.44 / Chapter 3.4.2 --- Simulation of Data Cube by B+ Tree --- p.48 / Chapter 3.5 --- Discussion --- p.54 / Chapter 4 --- Mining the N-most Interesting Itemsets --- p.56 / Chapter 4.1 --- Mining the N-most Interesting Itemsets --- p.56 / Chapter 4.1.1 --- Criteria of Mining the N-most Interesting itemsets --- p.56 / Chapter 4.1.2 --- Definition --- p.58 / Chapter 4.1.3 --- Property --- p.59 / Chapter 4.2 --- Method for Mining N-most Interesting Itemsets --- p.60 / Chapter 4.2.1 --- Algorithm --- p.60 / Chapter 4.2.2 --- Example --- p.76 / Chapter 4.3 --- Experiment --- p.81 / Chapter 4.3.1 --- Synthetic Data --- p.81 / Chapter 4.3.2 --- Real Data --- p.85 / Chapter 4.4 --- Discussion --- p.98 / Chapter 5 --- Conclusion --- p.100 / Bibliography --- p.101 / Appendix --- p.106 / Chapter A --- Programs for Mining the N-most Interesting Itemset --- p.106 / Chapter A.1 --- Programs --- p.106 / Chapter A.2 --- Data Structures --- p.108 / Chapter A.3 --- Global Variables --- p.109 / Chapter A.4 --- Functions --- p.110 / Chapter A.5 --- Result Format --- p.113 / Chapter B --- Programs for Mining the Multi-level Association Rules Using Data Cube --- p.114 / Chapter B.1 --- Programs --- p.114 / Chapter B.2 --- Data Structure --- p.118 / Chapter B.3 --- Variables --- p.118 / Chapter B.4 --- Functions --- p.119
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323175 |
| Date | January 2000 |
| Contributors | Kwong, Wang-Wai Renfrew., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xiv, 123 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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